, Volume 784, Issue 1, pp 93–109 | Cite as

Baited remote underwater video as a promising nondestructive tool to assess fish assemblages in clearwater Amazonian rivers: testing the effect of bait and habitat type

  • Kurt SchmidEmail author
  • José Amorim Reis-Filho
  • Euan Harvey
  • Tommaso Giarrizzo
Primary Research Paper


Baited remote underwater video (BRUV) systems are being used in marine ecosystems as a nonextractive, cost-effective method of assessing the fish fauna with minimal species bias. This technique has had limited applications in freshwater ecosystems. Rheophilic fish assemblages of the Xingu River, a clearwater Amazonian river in Northern Brazil, were sampled with BRUV systems. Two-hour video recordings were collected using five different bait treatments (sardine, croaker, cat food, sweet corn, and no bait) in two lotic habitat categories (rocky and sandy bottoms). A total of 2460 fish from 56 taxa and 13 families were recorded from the 80 BRUV deployments. Significantly different fish assemblages, species richness, and abundance were detected between habitat types and among treatments. Our results suggest that the use of crushed sardines as a standardized bait optimizes the sampling recording the highest species richness, relative abundance, and number of exclusive species of rheophilic fish in clearwater Amazonian rivers. The data also highlight the unique fish diversity of the Xingu River prior to the expected large-scale environmental degradation resulting from the forthcoming operation of the Belo Monte hydroelectric power plant.


Freshwater ecology Neotropical fish Lotic habitats Xingu River Brazil Amazon River basin Hydroelectric power plants 



We are grateful for the support provided by the Universidade Federal do Pará and the Grupo de Ecologia Aquática (GEA - Aquatic Ecology Group); Dr. L. M. Sousa from the laboratory of Ichthyology of Altamira for the fish pictures, A. J. S. Jesus for his support in data analysis; M. C. Andrade, D. A. Bastos, P. A. Trindade, and R. Oliveira for their help with species identification; and N. Balão for his skillful navigation. The first author was funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and is funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), the second author by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and the third author by Curtin University, Australia, and the last author receives a productivity grant from CNPq (process CNPq # 308278/2012–7) and is funded by CAPES and (Fundação Amazônia de Amparo a Estudos e Pesquisas do Pará) FAPESPA. Logistics for the field data collection were supported by Leme Engenharia Ltda.

Supplementary material

10750_2016_2860_MOESM1_ESM.pdf (531 kb)
Appendix 1 Average (± SD) first arrival time (mins) per bait and habitat type of reophilic fish species sampled with baited remote underwater video (BRUV) in the Xingu River. Appendix 2 Mean (± SD) relative abundance (MaxN) per bait and habitat type of reophilic fish species sampled with baited remote underwater video (BRUV) in the Xingu River. Supplementary material 1 (PDF 531 kb)


  1. Agostinho, A. A., L. C. Gomes & M. Zalewski, 2001. The importance of floodplains for the dynamics of fish communities of the upper River Paraná. Ecohydrology & Hydrobiologia 1: 209–217.Google Scholar
  2. Agostinho, A. A., F. M. Pelicice & L. C. Gomes, 2008. Dams and the fish fauna of the Neotropical region: impacts and management related to diversity and fisheries. Brazilian Journal of Biology 68: 1119–1132.CrossRefGoogle Scholar
  3. Anderson, M. J., 2001. Permutation tests for univariate or multivariate analysis of variance and regression. Canadian Journal of Fisheries and Aquatic Sciences 58: 626–639.CrossRefGoogle Scholar
  4. Anderson, M. J. & J. Santana-Garcon, 2015. Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecology Letters 18: 66–73.CrossRefPubMedGoogle Scholar
  5. Anderson, M. J., R. N. Gorley & K. R. Clarke, 2008. PERMANOVA + for PRIMER: Guide to Software and Statistical Methods. PRIMER-E, Plymouth.Google Scholar
  6. Andrew, N. L. & B. D. Mapstone, 1987. Sampling and the description of spatial pattern in marine ecology. Oceanography and Marine Biology, An Annual Review 25: 39–90.Google Scholar
  7. Barbosa, T. A. P., N. L. Benone, T. O. R. Begot, A. Gonçalves, L. Sousa, T. Giarrizzo, L. Juen & L. F. A. Montag, 2015. Effect of waterfalls and the flood pulse on the structure of fish assemblages of the middle Xingu River in the eastern Amazon basin. Brazilian Journal of Biology 75: 78–94.CrossRefGoogle Scholar
  8. Barthem, R. B. & N. N. Fabré, 2004. Biologia e diversidade dos recursos pesqueiros da Amazônia. In Ruffino, M. L. (ed.), A pesca e os recursos pesqueiros na Amazônia brasileira. Ibama/Provárzea, Manaus: 17–62.Google Scholar
  9. Barthem, R. B., M. C. L. B. Ribeiro & M. Petrere Jr., 1991. Life strategies of some long distance migratory catfish in relation to hydroelextric dams in the Amazon Basin. Biological Conservation 55: 339–345.CrossRefGoogle Scholar
  10. Bohlke, J. E., S. H. Weitzman & N. A. Menezes, 1978. Estado atual da sistemática de peixes de água doce da Ámerica do Sul. Acta Amazônica 8: 657–677.Google Scholar
  11. Bortone, S. A., R. W. Hastings & J. L. Oglesby, 1986. Quantification of reef fish assemblages: A comparison of several in situ methods. Northeast Gulf Science 1: 1–22.Google Scholar
  12. Botelho, M. C. & M. Camargo, 2010. Abundância de peixes de characiformes do médio rio Xingu, como indicador do rítmo de atividade diária em ambientes de lagos marginais. Boletim do Laboratório de Hidrobiologia 23: 25–48.Google Scholar
  13. Camargo, M., 2004. A comunidade ictica e suas interrelações tróficas como indicadores de integridade biológica na área de influência do projeto hidrelétrico Belo Monte-rio Xingu. Universidade Federal do Pará. Museu Paraense Emílio Goeldi, Belém, PA.Google Scholar
  14. Camargo, M., 2009. Os Consumidores: Peixes - Ecologia Trófica. In Camargo, M. & R. Ghilardi (eds), Entre a terra, as águas e os pescadores do médio rio Xingu: uma abordagem ecológica. Mauricio Camargo, Belém: 195–214.Google Scholar
  15. Camargo, M., T. Giarrizzo & V. Isaac, 2004. Review of the geographic distribution of fish fauna of the Xingu river basin, Brazil. ECOTROPICA 10: 123–147.Google Scholar
  16. Camargo, M., T. Giarrizzo & J. Carvalho Jr., 2005. Levantamento Ecológico Rápido da Fauna Íctica de Tributários do Médio-Baixo Rio Tapajós e Curuá. Boletim do Museu Paraense Emílio Goeldi. Série Ciências Naturais 2: 229–247.Google Scholar
  17. Camargo, M., H. Gimênes-Junior & L. Py-Daniel, 2012. Acaris Ornamentais do Médio Rio Xingu – Ornamental Plecos of the Middle Xingu River. FAPESPA, Belém. 177 p.Google Scholar
  18. Campbell, M. D., A. G. Pollack, C. T. Gledhill, T. S. Switzer & D. A. DeVries, 2015. Comparison of relative abundance indices calculated from two methods of generating video count data. Fisheries Research 170: 125–133.CrossRefGoogle Scholar
  19. Cappo, M. C. & I. W. Brown, 1996. Evaluation of sampling methods for reef fish populations of commercial, recreational interest. CRC Reef Research Technical report No. 6. CRC Technical No. 6. CRC Reef Research Centre. 72 p.Google Scholar
  20. Cappo, M. C., E. S. Harvey, H. A. Malcolm & P. J. Speare, 2003. Potential of video techniques to design and monitor diversity, abundance and size of fish in studies of Marine Protected Areas. In Beumer, J. P. & D. C. Smith (eds), Aquatic Protected Areas – What Works Best and How Do We Know?. World Congress on Aquatic Protected Areas, Cairns: 455–464.Google Scholar
  21. Cappo, M. C., P. J. Speare & G. De’ath, 2004. Comparison of baited remote underwater video stations (BRUVS) and prawn (shrimp) trawls for assessments of fish biodiversity in inter-reefal areas of the Great Barrier Reef Marine Park. Journal of Experimental Marine Biology and Ecology 302: 123–152.CrossRefGoogle Scholar
  22. Clarke, K. & R. Warwick, 2001. Changes in marine communities: an approach to statistical analysis and interpretation. PRIMER-E Ltd, Plymouth.Google Scholar
  23. Clarke, K. R. & R. N. Gorley, 2006. Primer v6: User Manual/Tutorial. PRIMER-E, Plymouth.Google Scholar
  24. Cruz, B. B., F. A. Teshima & M. Cetra, 2013. Trophic organization and fish assemblage structure as disturbance indicators in headwater streams of lower Sorocaba River basin, São Paulo, Brazil. Neotropical Ichthyology 11: 171–178.CrossRefGoogle Scholar
  25. Dorman, S. R., E. S. Harvey & S. J. Newman, 2012. Bait effects in sampling coral reef fish assemblages with stereo-BRUVs. PLoS One 7: e41538.CrossRefPubMedPubMedCentralGoogle Scholar
  26. Ebner, B. C. & D. L. Morgan, 2013. Using remote underwater video to estimate freshwater fish species richness. Journal of Fish Biology 82: 1592–1612.CrossRefPubMedGoogle Scholar
  27. Ebner, B. R., C. J. Fulton, S. Cousins, J. A. Donaldson, M. J. Kennard, J.-O. Meynecke & J. Schaffer, 2014. Filming and snorkelling as visual techniques to survey fauna in difficult to access tropical rainforest streams. Marine and Freshwater Research 66: 120.CrossRefGoogle Scholar
  28. Fitzpatrick, B. M., E. S. Harvey, A. J. Heyward, E. J. Twiggs & J. Colquhoun, 2012. Habitat specialization in tropical continental shelf demersal fish assemblages. PLoS One 7: e39634.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Fitzpatrick, C., D. McLean & E. S. Harvey, 2013. Using artificial illumination to survey nocturnal reef fish. Fisheries Research 146: 41–50.CrossRefGoogle Scholar
  30. Giakoumi, S. & G. D. Kokkoris, 2013. Effects of habitat and substrate complexity on shallow sublittoral fish assemblages in the Cyclades Archipelago, North-Eastern Mediterranean Sea. Mediterranean Marine Science 14: 58–68.CrossRefGoogle Scholar
  31. Giarrizzo, T., R. R. S. Oliveira, M. C. Andrade, A. P. Gonçalves, T. A. P. Barbosa, A. R. Martins, D. K. Marques, J. L. B. Santos, R. P. S. Frois, T. P. O. Albuquerque, L. F. A. Montag, M. Camargo & L. M. Sousa, 2015. Length–weight and length–length relationships for 135 fish species from the Xingu River (Amazon Basin, Brazil). Journal of Applied Ichthyology 31: 415–421.CrossRefGoogle Scholar
  32. Gladstone, W., S. Lindfield, M. Coleman & B. Kelaher, 2012. Optimisation of baited remote underwater video sampling designs for estuarine fish assemblages. Journal of Experimental Marine Biology and Ecology 429: 28–35.CrossRefGoogle Scholar
  33. Hardinge, J., E. S. Harvey, B. J. Saunders & S. J. Newman, 2013. A little bait goes a long way: The influence of bait quantity on a temperate fish assemblage sampled using stereo-BRUVs. Journal of Experimental Marine Biology and Ecology 449: 250–260.CrossRefGoogle Scholar
  34. Harvey, E. S., D. Fletcher & M. Shortis, 2002a. Estimation of reef fish length by divers and by stereo-video A first comparison of the accuracy and precision in the field on living fish under operational conditions. Fisheries Research 57: 255–265.CrossRefGoogle Scholar
  35. Harvey, E., M. Shortis, M. Stadler & M. Cappo, 2002b. A comparison of the accuracy and precision of measurements from single and stereo-video systems. Marine Technology Society Journal 36: 38–49.CrossRefGoogle Scholar
  36. Harvey, E., D. Fletcher, M. R. Shortis & G. A. Kendrick, 2004. A comparison of underwater visual distance estimates made by scuba divers and a stereo-video system: Implications for underwater visual census of reef fish abundance. Marine and Freshwater Research 55: 573–580.CrossRefGoogle Scholar
  37. Harvey, E. S., M. Cappo, J. J. Butler, N. Hall & G. A. Kendrick, 2007. Bait attraction affects the performance of remote underwater video stations in assessment of demersal fish community structure. Marine Ecology Progress Series 350: 245–254.CrossRefGoogle Scholar
  38. Harvey, E. S., J. J. Butler, D. L. McLean & J. Shand, 2012. Contrasting habitat use of diurnal and nocturnal fish assemblages in temperate Western Australia. Journal of Experimental Marine Biology and Ecology 426–427: 78–86.CrossRefGoogle Scholar
  39. Harvey, E. S., M. C. Cappo, G. A. Kendrick & D. L. Mclean, 2013a. Coastal fish assemblages reflect geological and oceanographic gradients within an Australian zootone. PLoS One 8: e80955.CrossRefPubMedPubMedCentralGoogle Scholar
  40. Harvey, E. S., D. McLean, S., Frusher, M. D. E. Haywood, S. J. Newman, & A. Williams, 2013b. The use of BRUVs as a tool for assessing marine fisheries and ecosystems: a review of the hurdles and potential. University of Western Australia. FRDC Report Project No. 2010/002.Google Scholar
  41. Heagney, E. C., T. P. Lynch, R. C. Babcock & I. M. Suthers, 2007. Pelagic fish assemblages assessed using mid-water baited video: standardising fish counts using bait plume size. Marine Ecology-Progress Series 350: 255–266.CrossRefGoogle Scholar
  42. Helfman, G. S., 1983. Underwater methods. In Nielsen, L. A. & D. L. Johnson (eds), Fisheries Techniques. American Fisheries Society, Bethesda: 349–369.Google Scholar
  43. Holmes, T. H., S. K. Wilson, M. J. Travers, T. J. Langlois, R. D. Evans, G. I. Moore, R. A. Douglas, G. Shedrawi, E. S. Harvey & K. Hickey, 2013. A comparison of visual and stereo-video based fish community assessment methods in tropical and temperate marine waters of Western Australia. Limnology and Oceanography: Methods 11: 337–350.CrossRefGoogle Scholar
  44. Jones, T., R. J. Davidson, J. P. A. Gardner & J. J. Bell, 2015. Evaluation and optimisation of underwater visual census monitoring for quantifying change in rock-reef fish abundance. Biological Conservation 186: 326–336.CrossRefGoogle Scholar
  45. Junk, W., M. Soares & P. Bailey, 2007. Freshwater fishes of the Amazon River basin: their biodiversity, fisheries, and habitats. Aquatic Ecosystem Health and Management 10: 153–173.CrossRefGoogle Scholar
  46. Kemenes, A. & B. R. Forsberg, 2014. Factors influencing the structure and spatial distribution of fishes in the headwater streams of the Jaú River in the Brazilian Amazon. Brazilian Journal of Biology 74: 23–32.CrossRefGoogle Scholar
  47. Langlois, T. J., E. S. Harvey, B. Fitzpatrick, J. J. Meeuwig, G. Shedrawi & D. L. S. Watson, 2010. Cost-efficient sampling of fish assemblages: comparison of baited video stations and diver video transects. Aquatic Biology 9: 155–168.CrossRefGoogle Scholar
  48. Lincoln Smith, M. P., 1989. Improving multispecies rocky reef fish censuses by counting different groups of species using different procedures. Environmental Biology of Fishes 26: 29–37.CrossRefGoogle Scholar
  49. Lindfield, S. J., E. S. Harvey, J. L. McIlwain & A. R. Halford, 2014. Silent fish surveys: bubble free diving highlights inaccuracies associated with SCUBA based surveys in heavily fished areas. Methods in Ecology and Evolution 5: 1061–1069.CrossRefGoogle Scholar
  50. Lowe-McConnell, R. H., 1987. Ecological Studies in Tropical Fish Communities. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
  51. Lowry, M., H. Folpp, M. Gregson & I. Suthers, 2012. Comparison of baited remote underwater video (BRUV) and underwater visual census (UVC) for assessment of artificial reefs in estuaries. Journal of Experimental Marine Biology and Ecology 416–417: 243–253.CrossRefGoogle Scholar
  52. Luckhurst, B. E. & K. Luckhurst, 1978. Analysis of the influence of substrate variables on coral reef fish communities. Marine Biology 49: 317–323.CrossRefGoogle Scholar
  53. Mallet, D. & D. Pelletier, 2014. Underwater video techniques for observing coastal marine biodiversity: A review of sixty years of publications (1952–2012). Fisheries Research 154: 44–62.CrossRefGoogle Scholar
  54. Myers, E. M. V., E. S. Harvey, B. J. Saunders & M. J. Travers, 2016. Fine-scale patterns in the day, night and crepuscular composition of a temperate reef fish assemblage. Marine Ecology. doi: 10.1111/maec.12336.Google Scholar
  55. Murphy, H. M. & G. P. Jenkins, 2010. Observational methods used in marine spatial monitoring of fishes and associated habitats: a review. Marine and Freshwater Research 61: 236–252.CrossRefGoogle Scholar
  56. Pelletier, D., K. Leleu, G. Mou-Tham, N. Guillemot & P. Chabanet, 2011. Comparison of visual census and high definition video transects for monitoring coral reef fish assemblages. Fisheries Research 107: 84–93.CrossRefGoogle Scholar
  57. R Core Team, 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  58. Santana-Garcon, J., S. J. Newman & E. S. Harvey, 2014a. Development and validation of a mid-water stereo-video technique for investigating pelagic fish assemblages. Journal of Experimental Marine Biology and Ecology 452: 82–90.CrossRefGoogle Scholar
  59. Santana-Garcon, J., M. J. Braccini, T. J. Langlois, S. J. Newman, R. B. McAuley & E. S. Harvey, 2014b. Calibration of pelagic stereo-BRUVs and scientific longline surveys for sampling sharks. Methods in Ecology and Evolution 5: 824–833.CrossRefGoogle Scholar
  60. Sawakuchi, A. O., G. A. Hartmann, H. O. Sawakuchi, F. N. Pupim, D. J. Bertassoli Jr., M. Parra, J. L. Antinao, L. M. Sousa, M. H. Sabaj Pérez, P. E. Oliveira, R. A. Santos, J. F. Savian, C. H. Grohmann, V. B. Medeiros, M. M. McGlue, D. C. Bicudo & S. B. Faustina, 2015. The Volta Grande do Xingu: Reconstruction of past environments and forecasting of future scenarios of a unique Amazonian fluvial landscape. Scientific Drilling 3: 1–12.Google Scholar
  61. Schobernd, Z. H., N. Bacheler & P. B. Conn, 2014. Examining the utility of alternative video monitoring metrics for indexing reef fish abundance. Canadian Journal of Fisheries and Aquatic Sciences 71: 464–471.CrossRefGoogle Scholar
  62. Schultz, A. L., H. A. Malcolm, D. J. Bucher & S. D. A. Smith, 2012. Effects of reef proximity on the structure of fish assemblages of unconsolidated substrata. PLoS One 7: 1–10.Google Scholar
  63. Sioli, H., 1984. The Amazon: Limnology and Landscape of a Mighty River and Its Basin. Monographiar Biologicae. Dr W. Junk Publisher, Dordrecht.CrossRefGoogle Scholar
  64. Smith, C. D., M. C., Quist, & R. S. Hardy, 2015. Fish assemblage structure and habitat associations in a large Western River system. River Research and Application. doi: 10.1002/rra.2877.
  65. Struthers, D. P., A. J. Danylchuk, D. M. W. Alexander & S. J. Cooke, 2015. Action cameras: bringing aquatic and fisheries research into view. Fisheries 40: 502–512.CrossRefGoogle Scholar
  66. Sabaj Pérez, M. H., 2015. Where the Xingu bends and will soon break. American Scientist 103: 395–403.CrossRefGoogle Scholar
  67. Underwood, A. J. & M. G. Chapman, 1998. A method for analysing spatial scales of variation in composition of assemblages. Oecologia 117: 570–578.CrossRefGoogle Scholar
  68. Underwood, A. J., M. G. Chapman & S. D. Connell, 2000. Observation in ecology: You can’t make progress on processes without understanding the patterns. Journal of Experimental Marine Biology and Ecology 250: 97–115.CrossRefPubMedGoogle Scholar
  69. Watson, D. L., E. S. Harvey, M. J. Anderson & G. A. Kendrick, 2005. A comparison of temperate reef fish assemblages recorded by three underwater stereo-video techniques. Marine Biology 148: 415–425.CrossRefGoogle Scholar
  70. Willis, T. J. & R. C. Babcock, 2000. A baited underwater video system for the determination of relative density of carnivorous reef fish. Marine and Freshwater Research 51: 755–763.CrossRefGoogle Scholar
  71. Willis, S., K. O. Winemiller & H. López-Fernández, 2005. Habitat structural complexity and morphological diversity of fish assemblages in a Neotropical floodplain river. Oecologia 142: 284–295.CrossRefPubMedGoogle Scholar
  72. Winemiller, K. O., P. McIntyre, L. Castello, E. Fluet-Chouinard, T. Giarrizzo, S. Nam, I. G. Baird, W. Darwall, N. K. Lujan, I. Harrison, M. L. J. Stiassny, R. A. M. Silvano, D. B. Fitzgerald, F. M. Pelicice, A. A. Agostinho, L. C. Gomes, J. S. Albert, E. Baran, M. Petrere Jr., C. Zarfl, M. Mulligan, J. P. Sullivan, C. Arantes, L. M. Sousa, A. A. Koning, D. J. Hoeinghaus, M. Sabaj, J. G. Lundberg, J. Armbruster, M. L. Thieme, P. Petry, J. Zuanon, G. Torrente Vilara, J. Snoeks, C. Ou, W. Rainboth, C. S. Pavanelli, A. Akama, A. van Soesberge & L. Sáenz, 2016. Hydropower expansion in the Amazon, Congo and Mekong– a looming threat to global biodiversity. Science 351: 128–129.CrossRefPubMedGoogle Scholar
  73. Wraith, J. A., 2007. Assessing reef fish assembalges in a temperate marine park using baited remote underwater video. MSc. thesis, School of Biological Sciences, University of Wollongong.Google Scholar
  74. Wraith, J. A., T. Lynch, T. E. Minchinton, A. Broad & A. R. Davis, 2013. Bait type affects fish assemblages and feeding guilds observed at baited remote underwater video stations. Marine Ecology Progress Series 477: 189–199.CrossRefGoogle Scholar
  75. Zuanon, J., 1999. A história natural da íctiofauna de corredeiras do rio Xingu, na região de Altamira, Pará. Tese de doutorado, UNICAMP, Campinas, SP.Google Scholar
  76. Zintzen, V., M. J. Anderson, C. D. Roberts, E. S. Harvey, A. L. Stewart & C. D. Struthers, 2012. Diversity and composition of demersal fishes along a depth gradient assessed by baited remote underwater stereo-video. PLoS One 7(10): e48522.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Laboratório de Biologia PesqueiraUniversidade Federal do Pará (UFPA)BelémBrazil
  2. 2.Laboratório de Ecologia Bentônica, Instituto de BiologiaUniversidade Federal da Bahia (UFBA)SalvadorBrazil
  3. 3.Department of Environment and AgricultureCurtin UniversityPerthAustralia

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